Autonomous Parts Replenishment With AMR Fleet Feedback Control
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Solution Overview
Problem
Industrial manufacturing processes face inefficiencies in provision replenishment due to uncertainties in part delivery and resource allocation across multiple queues, which can lead to increased queue sizes and reduced productivity.
Innovation Solution
A method and system for autonomous provision replenishment using self-driving material-transport vehicles equipped with processors and sensors, which receive provision-replenishment signals to determine pick-up and drop-off paths, navigate around obstacles, and manage missions based on consumption rates in manufacturing processes, allowing for efficient delivery of parts to intermediate stocking queues.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If parts are stored in a storage location away from the assembly line and brought to a temporary location, then parts availability is improved, but the number of parts in buffer increases and manufacturing efficiency decreases
Solution Approach 1:
The system uses autonomous mobile robots that self-navigate to pick-up locations, automatically receive parts, and deliver them to drop-off locations without human intervention. The robots autonomously determine their own paths and execute delivery missions, enabling the system to serve itself rather than requiring manual material handling operations.
Solution Approach 2:
The patent replaces traditional manual material handling (human operators physically moving parts) with autonomous mobile robots equipped with sensors and navigation systems. This substitution eliminates the need for large buffers while maintaining parts availability, as robots can dynamically respond to replenishment signals and deliver parts just-in-time.
2Productivity
If queue size is reduced towards just-in-time process, then manufacturing efficiency is improved, but uncertainties in part delivery increase
Solution Approach 1:
The system implements a feedback mechanism where consumption of parts at the assembly line generates replenishment signals that are immediately transmitted to the fleet management system. This real-time feedback enables the system to respond dynamically to actual consumption rates, maintaining small queues while ensuring parts are delivered promptly based on actual demand rather than predetermined schedules.
Solution Approach 2:
The fleet management system pre-coordinates delivery missions by determining optimal vehicle assignments and routes before execution. This preliminary planning ensures that even with small queues, parts delivery is certain and timely, as the system proactively manages the autonomous vehicle fleet to meet upcoming replenishment needs.
3Productivity
If the same forklift serves multiple stages, then resource utilization is improved, but delivery uncertainties increase
Solution Approach 1:
The system dynamically assigns autonomous vehicles to delivery missions based on real-time conditions, vehicle locations, and mission priorities. Unlike static forklift assignments, the fleet management system can reassign vehicles dynamically, allowing multiple vehicles to serve multiple stages flexibly. This dynamic allocation maintains high resource utilization while ensuring delivery certainty through adaptive routing and assignment.
Solution Approach 2:
The patent segments the material handling function into multiple independent autonomous vehicles rather than using a single shared forklift. This segmentation allows parallel execution of multiple delivery missions simultaneously, improving both resource utilization and delivery certainty, as each vehicle independently executes its assigned missions without interfering with others.
Data Source
AI summary
Systems and methods for autonomous provision replenishment are disclosed. Parts used in a manufacturing process are stored in an intermediate stock queue. When the parts are consumed by the manufacturing process and the number of parts in the queue falls below a threshold, a provision-replenishment signal is generated. One or more self-driving material-transport vehicles, a fleet-management system, and a provision-notification device.